Observability and tracing · Lesson 10 of 15 · 8 min

Review model saturation

Saturation combines utilization, queueing and service time. High utilization without queue growth can mean useful work; a growing queue gives the observation operational context.

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learner@clairos:~ $

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Your objectives

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1
Read the case evidence
Read /opt/ai-lab/cases/aiops-observability-tracing-010.json. Separate the recorded facts from your assumptions.
Hint & worked example
Saturation combines utilization, queueing and service time. High utilization without queue growth can mean useful work; a growing queue gives the observation operational context.
The complete case JSON is read successfully.
```
cat /opt/ai-lab/cases/aiops-observability-tracing-010.json
```
Common mistake to avoid: High GPU utilization alone proves a hardware failure.
2
Isolate the deciding signal
Select the queue_after field from the case JSON. What does it establish—and what does it not establish?
Hint & worked example
Review overload because the queue and latency are rising alongside high utilization.
A successful JSON query isolates queue_after.
```
jq .queue_after /opt/ai-lab/cases/aiops-observability-tracing-010.json
```
Common mistake to avoid: Using a different case or an unrelated signal as the justification.
3
Record your operational decision
Choose the justified code: review-saturation, change-without-evidence, or ignore-the-signal. Save that one code followed by a newline in /home/learner/lab/aiops-observability-tracing-010-decision.txt.
Hint & worked example
Review overload because the queue and latency are rising alongside high utilization.
The decision artifact contains the correct code, not just a matching command in history.
```
printf '%s\n' 'review-saturation' > /home/learner/lab/aiops-observability-tracing-010-decision.txt
```
Common mistake to avoid: High GPU utilization alone proves a hardware failure.

Understand the pattern

Saturation combines utilization, queueing and service time. High utilization without queue growth can mean useful work; a growing queue gives the observation operational context.

Interpret the evidence

Review overload because the queue and latency are rising alongside high utilization.

A tempting mistake

High GPU utilization alone proves a hardware failure.

All names, measurements, prices, versions, and thresholds in this exercise are fictional training data. The evidence supports this bounded scenario, not a claim about a live provider.

Worked example and expected result

$ cat /opt/ai-lab/cases/aiops-observability-tracing-010.json

{
  "fixture": "Fictional training data; not a live measurement.",
  "gpu_util_percent": 98,
  "queue_before": 4,
  "queue_after": 30,
  "p95_rising": true
}

Exit status: 0. The command completed successfully in the training fixture.

$ jq .queue_after /opt/ai-lab/cases/aiops-observability-tracing-010.json

Exit status: 0. The command completed successfully in the training fixture.

$ printf '%s\n' 'review-saturation' > /home/learner/lab/aiops-observability-tracing-010-decision.txt

(no output; exit 0)

Exit status: 0. The command completed successfully in the training fixture.

Try a variation

Name the assumption that would have to change for “review-saturation” to stop being the justified action. Do not silently replace missing evidence with a guess.

Explain it

What does the evidence in “Review model saturation” justify?High GPU utilization alone proves a hardware failure.Make the change now and gather the missing evidence afterward.Review overload because the queue and latency are rising alongside high utilization.

Apply it elsewhere

A colleague concludes: “High GPU utilization alone proves a hardware failure.” What should you do?Treat this fixture as a live measurement.Reject that conclusion; return to the case evidence and its stated limits.Accept it because the training command completed.

Keep the momentum.

Complete the lab, then explain and apply the pattern.

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Case methodology: evidence, limits, decision